To enlarge the search space, we generate the generalized version of standard DenseNets by randomly selecting channels for concatenation. Specifically, for a given cell \(c\) , we define \(t_c\)  as the maximum skip connections that each layer can have; thus, we use \(t_c\)  to control the topological properties of CNNs. Given the definition of \(t_c\) , layer \(i\)  can receive DenseNet-type skip connections (DTSC) from a maximum number of \(t_c\)  channels from previous layers within the same cell; that is, we randomly select \(min\lbrace w_c(i-1), t_c\rbrace \)  channels from layers \({0,1,...,(i-2)}\) , and concatenate them at layer \(i-1\) . The concatenated channels then pass through a convolutional layer to generate the output of layer \(i\)  (\(s_i\) ). Similar to recent NAS research [1]}, we select links randomly because random architectures are often as competitive as the carefully designed ones. If the skip connections encompass all-to-all connections, this would result in the original DenseNet architecture [2]}. An important advantage of the above setup is that we can control the number of DTSC (using \(t_c\) ) to cover a vast search space with a large number of candidate DNNs.
